A rock failure combined early warning method based on beta value and b value

Abstract

The application provides a rock damage joint early warning method based on a beta value and a b value, and the early warning method comprises the following steps: installing an acoustic emission / microseismic sensor on the surface of a rock to be measured to obtain waveform signals in real time when the rock to be measured is damaged, and calculating characteristic parameters RA and AF of the waveform; determining a rock fracture mechanism proportion when the rock to be measured is fractured according to the characteristic parameters RA and AF of the waveform, and statistically analyzing the rock fracture mechanism proportion to obtain a stable statistical value beta; calculating a magnitude according to the waveform, and calculating a b value through a G-R method; calculating the most relevant coefficient of the beta value and the b value, and compensating the b value to the beta value through a compensation function to eliminate the interference signal fluctuation in the quiet period, so that an early warning coefficient that is stable in the quiet period and sharply increases in the damage period is obtained, and the accuracy of rock damage early warning is improved.

Description

Technical Field

[0001] This invention belongs to the field of rock damage early warning technology, and specifically relates to a joint early warning method for rock damage based on β value and b value. Background Technology

[0002] Acoustic emission / microseismic monitoring technology is a three-dimensional spatial monitoring technology for the vibration of rock or rock mass fractures, and it has developed rapidly in recent years. It can not only analyze the time, location, and magnitude of rockburst events through acoustic wave analysis, but also, highly sensitive microseismic monitoring systems can capture precursory events of rock micro-fractures smaller than the magnitude of a rockburst, thus providing potential for predicting and forecasting the risk of surrounding rock damage in rock engineering projects. With the gradual advancement of infrastructure construction in Southwest my country and the increasing exploration of deeper parts of the earth, deep rock engineering projects are becoming more frequent. Furthermore, with increasing burial depth, the geological environment and stress state are becoming increasingly complex, and the impact of disasters such as rockbursts, spalling, and collapses caused by excavation is becoming increasingly prominent. At the same time, the timing and scale of surrounding rock fractures are becoming more difficult to predict. Therefore, more effective monitoring and early warning methods are needed to minimize economic losses and casualties.

[0003] Currently, methods for monitoring rock mass damage and failure mainly fall into three categories: stress monitoring and inversion, multi-point displacement monitoring of rock mass, and acoustic emission / microseismic monitoring. Stress monitoring and inversion involves densely deploying stress monitoring sensors at the engineering site to obtain the changes in multiple points of the rock mass over time. Numerical simulation is then used to adjust boundary stresses for inversion, resulting in a stress cloud map of the entire engineering area for early warning. However, existing stress sensors generally calculate stress based on strain, leading to significant errors in stress values. Furthermore, due to limitations in the working principle of stress gauges, stress sensors cannot be retrieved after deployment, posing a challenge when the monitoring area needs to be relocated. When moving excavation areas, a large number of sensors are required, increasing costs and necessitating a reduction in monitoring density. However, the combined effect of monitoring errors and reduced density affects inversion accuracy and early warning precision. Multi-point displacement monitoring of rock mass involves deploying multiple displacement gauges to monitor rock deformation. However, the gauges are arranged linearly in a cross-sectional divergent pattern, limiting their monitoring range to their path and thus significantly restricting the overall rock mass coverage. Acoustic emission / microseismic monitoring can receive rupture vibrations across the entire engineering rock mass in real time. Signal analysis allows for accurate and rapid early warning of surrounding rock failure. Currently, acoustic emission early warning often relies on the b-value. Many existing studies on seismology and rock failure indicate that rock failure is imminent when the b-value approaches 1. However, the b-value only considers magnitude, which is unstable during non-warning periods and is prone to misinterpretation, leading to incorrect early warning timing.

[0004] Therefore, how to provide a joint early warning method for rock failure based on β and b values, by statistically analyzing the fracture mechanism parameters of vibration signals and combining them with b values, to obtain an early warning coefficient that is stable during the calm period and increases sharply during the failure period, thereby improving the accuracy of rock failure early warning, is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] The purpose of this invention is to provide a joint early warning method for rock damage based on β and b values, so as to solve at least one of the above-mentioned technical problems.

[0006] To achieve the above objectives, the first aspect of the present invention provides a joint early warning method for rock damage based on β and b values. The method includes: installing an acoustic emission / microseismic sensor on the surface of the rock to be tested to acquire the waveform when the rock fractures and calculating waveform characteristic parameters; determining the proportion of fracture mechanisms when the rock fractures based on the waveform characteristic parameters; performing statistical analysis on the proportion of fracture mechanisms to obtain a stationary statistical value β of the proportion of fracture mechanisms; calculating the rock fracture magnitude according to the waveform characteristic parameters, and calculating the b value using the GR method; calculating the most correlation coefficient between the β value and the b value, and compensating the b value to the β value using a compensation function to obtain an early warning coefficient, thereby realizing early warning of rock damage.

[0007] In the first aspect, the acoustic emission / microseismic sensor is connected to a monitoring and processing system, which includes: an acoustic emission / microseismic sensing device, a data connection line, a signal conversion and preprocessing device, and a signal analysis device. The acoustic emission / microseismic sensing device is used to receive waveform signal data when the rock under test fractures, and transmits it to the signal conversion and preprocessing device through the data connection line. The signal analysis device is communicatively connected to the signal conversion and preprocessing device and is used to calculate the waveform characteristic parameters of the rock under test. The waveform characteristic parameters include amplitude, average frequency, rise time, and energy.

[0008] In the first aspect, the acoustic emission / microvibration sensing device includes: an elastic wave receiver for converting the vibration signal of the elastic wave generated when the rock under test fractures into an electrical analog signal.

[0009] In the first aspect, the signal conversion and preprocessing device includes a signal converter and a signal preprocessor; the signal converter converts the received analog electrical signal into a data signal with a specific sampling frequency; the signal preprocessor extracts the waveform data of the rock under test being damaged from the data signal through a preset threshold and transmits it to the signal analysis device.

[0010] In the first aspect, the formula for calculating the proportion of the rupture mechanism is:

[0011]

[0012] Where AF is the calculated average frequency of the waveform, RT is the calculated rise time of the waveform, and A is the calculated amplitude of the waveform.

[0013] The formula for calculating the magnitude is:

[0014]

[0015] Among them, A dB This represents the maximum amplitude of the calculated waveform.

[0016] In the first aspect, the expression for calculating the stationary statistic β of the proportion of the rupture mechanism is:

[0017]

[0018] Where, k i N represents the percentage of the rupture mechanism for the i-th rupture within a certain time window. i The ranking of the proportion of elastic wave failure mechanisms generated by the i-th rupture within a certain time window.

[0019] In the first aspect, the expression for calculating the value of b is:

[0020]

[0021] Among them, M i Let be the acoustic emission magnitude of the i-th rock fracture within a certain time window.

[0022] In the first aspect, the calculation of the correlation coefficient between the β value and the b value, and the compensation of the b value to the β value through a compensation function to obtain an early warning coefficient, thereby realizing the early warning of damage to the rock under test, includes: shifting the time axis of the b value to form different time differences with the time axis of the β value, calculating the correlation coefficient between the b value and the β value under different time differences, determining the compensation time difference through the maximum value of the correlation coefficient, and based on the compensation time difference, using a compensation function to compensate the b value to the β value to obtain an early warning coefficient, thereby realizing the early warning of damage to the rock under test.

[0023] Beneficial effects:

[0024] This invention provides a joint early warning method for rock failure based on β and b values. First, acoustic emission / microseismic sensors are installed on the surface of the rock to be tested to acquire waveform signals when the rock fractures in real time. Then, characteristic parameters RA and AF of the waveform are calculated based on the waveform, and the proportion of fracture mechanisms during rock fracture is determined based on the characteristic parameters. Statistical analysis is performed on the proportion of fracture mechanisms to obtain a stationary statistical value β. The rock fracture magnitude is calculated based on the waveform characteristic parameters, and the b value is calculated using the GR method. Finally, the most correlation coefficient between β and b values ​​is calculated, and the b value is compensated for by a compensation function, thereby obtaining an early warning coefficient that is stable during the calm period and sharply increases during the failure period, thus achieving early warning of rock failure. This invention, by installing acoustic emission / microseismic sensors on the surface of the rock to be tested, can acquire the waveform of the rock fracture over a wide range, with high precision and in real time, calculate the waveform characteristic parameters RA, AF and magnitude, obtain the stationary statistical value β of the proportion of the fracture mechanism of the rock to be tested through statistical analysis, and use a compensation function to compensate the β value with the b value, thereby eliminating parameter fluctuations during the quiet period, reducing false alarms caused by interference signals, and thus improving the accuracy of early warning of rock damage. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of this specification or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 A schematic flowchart of a joint early warning method for rock damage based on β and b values ​​provided by the present invention;

[0027] Figure 2 This is a schematic diagram of the acoustic emission / microvibration sensor provided in Embodiment 1 of the present invention;

[0028] Figure 3 This is a graph showing the distribution density changes of the fracture mechanism proportion parameter and the β value in Embodiment 1 of the present invention;

[0029] Figure 4 This is a graph showing the distribution density variation of earthquake magnitude and the value of b in Embodiment 1 of the present invention;

[0030] Figure 5 This is a graph showing the correlation coefficient between the β and b values ​​under different time differences in Embodiment 1 of the present invention.

[0031] Figure 6 This is a graph of the curve of the β value without b-value contrast compensation in Embodiment 1 of the present invention;

[0032] Figure 7 This is a curve of the β value after b-value contrast compensation in Embodiment 1 of the present invention;

[0033] Figure label:

[0034] 1. Sound generation / micro-vibration sensing device; 2. Data connection cable; 3. Monitored rock mass; 4. Monitored rock; 5. Signal conversion and preprocessing device; 6. Signal analysis device. Detailed Implementation

[0035] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0036] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or device that includes a series of steps or modules is not necessarily limited to those explicitly listed, but may include other steps or modules not explicitly listed or inherent to such processes, methods, products, or devices. The naming or numbering of steps appearing in this application does not imply that the steps in the method flow must be performed in the chronological / logical order indicated by the naming or numbering. The execution order of named or numbered process steps can be changed according to the desired technical purpose, as long as the same or similar technical effect is achieved.

[0037] The module division described in this application is a logical division. In practical applications, there may be other division methods. For example, multiple modules may be combined or integrated into another system, or some features may be ignored or not executed. In addition, the coupling or direct coupling or communication connection between modules shown or discussed may be through some interfaces, and the indirect coupling or communication connection between modules may be electrical or other similar forms, none of which are limited in this application. Furthermore, the modules or sub-modules described as separate components may or may not be physically separated, may or may not be physical modules, or may be distributed in multiple circuit modules. Some or all of the modules may be selected to achieve the purpose of the solution in this application according to actual needs.

[0038] Example 1

[0039] Please see Figure 1 This invention provides a joint early warning method for rock damage based on β and b values. The method includes: installing an acoustic emission / microseismic sensor on the surface of the rock to be tested to acquire the waveform when the rock fractures and calculating waveform characteristic parameters; determining the proportion of fracture mechanisms when the rock fractures based on the waveform characteristic parameters; performing statistical analysis on the proportion of fracture mechanisms to obtain a stationary statistical value β of the proportion of fracture mechanisms; calculating the rock fracture magnitude according to the waveform characteristic parameters, and calculating the b value using the GR method; calculating the most correlation coefficient between the β value and the b value, and compensating the b value to the β value using a compensation function to obtain an early warning coefficient, thereby realizing early warning of rock damage.

[0040] Specifically, this invention provides a joint early warning method for rock failure based on β and b values. First, acoustic emission / microseismic sensors are installed on the surface of the rock to be tested to acquire waveform signals when the rock fractures in real time. Then, characteristic parameters RA and AF of the waveform are calculated, and the proportion of fracture mechanisms during rock fracture is determined based on these parameters. Statistical analysis of the fracture mechanism proportion is performed to obtain a stationary statistical value β. The rock fracture magnitude is calculated based on the waveform characteristic parameters, and the b value is calculated using the GR method. Finally, the most correlation coefficient between β and b values ​​is calculated, and the b value is compensated for by a compensation function, thereby obtaining an early warning coefficient that is stable during the calm period and sharply increases during the failure period, thus achieving early warning of rock failure. This invention, by installing acoustic emission / microseismic sensors on the surface of the rock to be tested, can acquire the waveform of the rock fracture over a wide range, with high precision and in real time, calculate the waveform characteristic parameters RA, AF and magnitude, obtain the stationary statistical value β of the proportion of the fracture mechanism of the rock to be tested through statistical analysis, and use a compensation function to compensate the β value with the b value, thereby eliminating parameter fluctuations during the quiet period, reducing false alarms caused by interference signals, and thus improving the accuracy of early warning of rock damage.

[0041] In some possible implementations, such as Figure 2 As shown, the acoustic emission / microseismic sensor is connected to a monitoring and processing system, which includes: an acoustic emission / microseismic sensor 1, a data connection line 2, a signal conversion and preprocessing device 5, and a signal analysis device 6. The acoustic emission / microseismic sensor 1 is used to receive waveform signal data when the rock under test fractures, and transmits it to the signal conversion and preprocessing device 5 through the data connection line 2. The signal analysis device 6 is communicatively connected to the signal conversion and preprocessing device 5 and is used to calculate the waveform characteristic parameters of the rock under test. The waveform characteristic parameters include amplitude, average frequency, rise time, and energy.

[0042] In some possible implementations, the acoustic emission / microvibration sensing device 1 includes: an elastic wave receiver for converting the vibration signal of the elastic wave generated when the rock under test fractures into an electrical analog signal.

[0043] In some possible implementations, the signal conversion and preprocessing device 5 includes a signal converter and a signal preprocessor; the signal converter converts the received analog electrical signal into a data signal with a specific sampling frequency; the signal preprocessor extracts the waveform data of the rock under test when it is damaged from the data signal through a preset threshold and transmits it to the signal analysis device.

[0044] Specifically, the acoustic emission / microseismic sensor is connected to the monitoring and processing system, which includes an acoustic emission / microseismic sensor device 1, a data connection line 2, a signal conversion and preprocessing device 5, and a signal analysis device 6. The acoustic emission / microseismic sensor and the acoustic emission / microseismic sensor device 1 are installed in the borehole of the monitored rock mass 3 or on the surface of the monitored rock 4, and coupled through cement mortar or a recycling device. Furthermore, the acoustic emission / microseismic sensor device 1 includes an elastic wave receiver. When the rock under test (monitored rock mass 3 or monitored rock 4) fractures, it generates elastic waves. When the elastic waves propagate to the acoustic emission / microseismic sensor, the elastic wave receiver converts the vibration signal of the received elastic waves into an electrical analog signal. The acoustic emission / micro-vibration transmission device 1 and the signal conversion and preprocessing device 5 are connected via a data connection line 2. The data connection line 2 transmits the output analog electrical signal to the signal conversion and preprocessing device 5. Furthermore, the signal conversion and preprocessing device 5 includes a signal converter and a signal preprocessor. The signal converter converts the received analog electrical signal into a digital signal with a specific sampling frequency. The signal preprocessor extracts the waveform data at the moment of fracture through a preset threshold and calculates parameters such as the amplitude, average frequency, rise time, and energy of the elastic wave generated when the rock under test fractures. The signal analysis device 6 is communicatively connected to the signal conversion and preprocessing device 5 and is used to receive the elastic wave waveform data output from the signal preprocessor, including parameters such as amplitude, average frequency, rise time, and energy.

[0045] In some possible implementations, the formula for calculating the proportion of the rupture mechanism is:

[0046]

[0047] Where AF is the calculated average frequency of the waveform, RT is the calculated rise time of the waveform, and A is the calculated amplitude of the waveform.

[0048] The formula for calculating the magnitude is:

[0049]

[0050] Among them, A dB This represents the maximum amplitude of the calculated waveform.

[0051] Specifically, the RA-AF method is used to calculate the proportion parameter k of the fracture mechanism of the rock under test, determining whether the main fracture mechanism is tensile or shear failure. k1 is defined as the boundary between tensile and shear failure. When k > k1 at the time of rock fracture, tensile failure is the dominant mechanism; when k < k1, shear failure is the dominant mechanism. However, in the rock fracture process, there is no purely tensile or shear failure; tensile-shear failure is a continuous process. Therefore, when a rock fractures, the RA-AF method is used to determine the dominant mechanism. The proportion of fracture mechanisms in the rock under test is determined by a positive correlation with the control ratio of tensile failure. Furthermore, in acoustic emission / microseismic techniques, the magnitude of the rock fracture is typically calculated by dividing the maximum amplitude by 20.

[0052] In some possible implementations, the stationary statistic β representing the proportion of the rupture mechanism is calculated as follows:

[0053]

[0054] Where, k i N represents the percentage of the rupture mechanism for the i-th rupture within a certain time window. i The magnitude of the elastic wave generated by the i-th rupture within a certain time window is ranked.

[0055] In this embodiment, a statistical analysis of the rupture mechanism proportion parameter k is performed using a method similar to GR to obtain a stationary statistical value β. Specifically, a time window is selected to include a certain duration or the number of elastic waves generated. The rupture mechanism proportion parameter k of the elastic wave waveform within the time window is statistically analyzed. Then, within a selected time window, the rupture mechanism proportion of the i-th rupture within the time window is determined as k. i The number of times the rock fractured within the time window was counted as N. i As the time window changes, k i and N i All will change, and N i With lgk i It exhibits a linear relationship, such as Figure 3 As shown, the following results were obtained using the least squares method for linear fitting:

[0056] N = α + βlgk;

[0057] The fitted slope β is the stationary statistical value of the proportion of the rupture mechanism. The above linear fitting formula can be transformed to obtain the following calculation expression:

[0058]

[0059] In the above calculation expression, the smaller the slope, the higher the proportion of rock failure dominated by shear failure. A β can be calculated for each time window. By selecting different time windows as the time axis changes, the curve of β changing with time or the progress of the experiment can be obtained.

[0060] In some possible implementations, the expression for calculating the value of b is:

[0061]

[0062] Among them, M i Let be the acoustic emission magnitude of the i-th rock fracture within a certain time window.

[0063] In acoustic emission / microseismic technology, the b-value of acoustic emission / microseismic signals is typically calculated using the following GR relationship:

[0064] lgN = a + bM;

[0065] Where M represents the rock fracturing magnitude, N represents the number of rock failures occurring within the range of M+ΔM, and a and b are constants; when the value of b is close to 1, the rock will fail. However, this method of judging rock failure only uses magnitude as a parameter, making fluctuations unstable during the middle of loading (i.e., the calm period) and accompanied by erroneous signals, causing the rock failure early warning to fail. Based on this, the maximum likelihood method is used to estimate the value of b, such as... Figure 4 As shown, the following calculation expression is obtained:

[0066]

[0067] The above calculation expression can be used to calculate the b value of the acoustic emission / microseismic signal at different times as the time window moves, so as to provide a basis for subsequent β value correction.

[0068] In some possible implementations, the step of calculating the correlation coefficient between the β value and the b value, and compensating the b value with the β value using a compensation function to obtain an early warning coefficient, thereby realizing early warning of damage to the rock under test, includes: shifting the time axis of the b value to form different time differences with the time axis of the β value, calculating the correlation coefficient between the b value and the β value under different time differences, determining the compensation time difference by the maximum value of the correlation coefficient; and based on the compensation time difference, using a compensation function to compensate the b value with the β value to obtain an early warning coefficient, thereby realizing early warning of damage to the rock under test.

[0069] Specifically, the time axis of the b-value is shifted to create different time differences with the time of the β-value. The correlation coefficient between the b-value and the β-value is calculated under different time differences, and the time difference when the correlation coefficient reaches its maximum value is selected as the compensation duration. Figure 5 As shown, this provides the conditions for the b-value to correct the β-value.

[0070] Furthermore, by compensating the b-value with the β-value using a compensation function, an early warning coefficient γ is obtained, thereby achieving early warning of rock damage. This application includes, but is not limited to, using a sigmoid activation function or a linear function to compensate for the b-value and β-value. In a specific embodiment, the sigmoid activation function is used to compensate the nonlinear inverse of the b-value with the β-value to obtain the early warning coefficient γ, thereby achieving early warning of rock fracture. The early warning coefficient is shown below:

[0071]

[0072] When the b value is much higher than 1.5, the probability of failure is small, the activation function is activated, and the b value will negatively compensate for the β value, reducing the risk of early warning. When the b value is close to 1.5, the surrounding rock is in a state of gradual failure, the activation function is not activated, and there will be no negative compensation for the β value. When the b value is less than 1.5 and close to or even lower than 1, the rock is about to fail or has already failed, the activation function is activated, and it will positively compensate for the β value, improving the sensitivity of early warning.

[0073] Alternatively, the value of b can be compensated for by a linear function with contrast to the value of β, and the warning coefficient is shown below:

[0074] γ = p × b + β;

[0075] Where p is the first compensation coefficient, used to adjust the degree of nonlinearity of the compensation; q is the second compensation coefficient, used to adjust the degree of compensation.

[0076] like Figure 6-7 As shown, the curve obtained by compensating the difference between the b value and the β value through the sigmoid activation function has the characteristics of being stable during the rock calm period and changing drastically near the failure period, thus eliminating the interference signal fluctuations during the calm period and obtaining an early warning coefficient that is stable during the calm period and increases sharply during the failure period.

[0077] In summary, this invention provides a joint early warning method for rock failure based on β and b values. Acoustic emission sensors or microseismic sensors (elastic wave receivers) are installed on the surface of the rock specimen or engineering rock mass to be monitored, and the installed sensors are connected to a monitoring and processing system. Through real-time monitoring, source parameters such as rise time, maximum amplitude, average frequency, and magnitude of the rock fracture event waveform are obtained, and the change in b value is calculated. Based on the source mechanism, the critical effect of increased tensile and shear failure is distinguished, and the proportion k of the rock fracture mechanism is calculated. By calculating the proportion k of the rock fracture mechanism and performing normalization, a stationary statistical value β is obtained by using linear fitting to obtain the slope. By taking the correlation coefficients of b and β values ​​at different time shifts, and taking the time shift when the correlation coefficient is maximum, the b value is normalized and negatively compensated for by the sigmoid function, resulting in an early warning coefficient that is stable during the calm period and sharply increases during the failure period. Furthermore, the early warning method provided by this invention can also be applied to the monitoring and early warning of stability projects involving rock damage, such as hydropower projects, tunnel projects, geothermal projects, oil storage projects, open-pit slope safety projects, oil and gas development, and monitoring of rock bursts and collapses caused by mining.

[0078] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A joint early warning method for rock failure based on β and b values, characterized in that, The method includes: An acoustic emission / micro-vibration sensor is installed on the surface of the rock to be tested to acquire the waveform when the rock fractures and to calculate the waveform characteristic parameters. The proportion of fracture mechanisms during the fracture of the rock under test is determined based on the waveform characteristic parameters. A statistical analysis was performed on the proportion of the fracture mechanism to obtain a stationary statistical value β of the proportion of the fracture mechanism; The rock fracture magnitude is calculated based on the waveform characteristic parameters, and the b value is calculated using the GR method. The most correlation coefficient between the β value and the b value is calculated, and the b value is compensated to the β value through a compensation function to obtain the early warning coefficient, thereby realizing the early warning of damage to the rock under test; The acoustic emission / micro-vibration sensor is connected to the monitoring and processing system, which includes: an acoustic emission / micro-vibration sensor, a data connection line, a signal conversion and preprocessing device, and a signal analysis device. The acoustic emission / micro-vibration sensing device is used to receive waveform signal data when the rock under test fractures, and transmits it to the signal conversion and preprocessing device through the data connection line; The signal analysis device is communicatively connected to the signal conversion and preprocessing device and is used to calculate the waveform characteristic parameters of the rock under test. The waveform characteristic parameters include amplitude, average frequency, rise time, and energy; The formula for calculating the proportion of the rupture mechanism is as follows: Where AF is the calculated average frequency of the waveform, RT is the calculated rise time of the waveform, and A is the calculated amplitude of the waveform. The formula for calculating the magnitude is: Among them, A dB The maximum amplitude of the calculated waveform; The expression for calculating the stationary statistic β of the proportion of the rupture mechanism is as follows: Where, k i N represents the percentage of the rupture mechanism for the i-th rupture within a certain time window. i Ranking the proportion of elastic wave rupture mechanisms generated by the i-th rupture within a certain time window; The expression for calculating the value of b is: Among them, M i Let be the acoustic emission magnitude of the i-th rock fracture within a certain time window.

2. The rock damage joint early warning method based on β and b values ​​according to claim 1, characterized in that, The acoustic emission / micro-vibration sensing device includes: an elastic wave receiver, used to convert the vibration signal of the elastic wave generated when the rock under test fractures into an electrical analog signal.

3. The rock damage joint early warning method based on β and b values ​​according to claim 2, characterized in that, The signal conversion and preprocessing device includes a signal converter and a signal preprocessor; the signal converter converts the received analog electrical signal into a data signal with a specific sampling frequency; the signal preprocessor extracts the waveform data of the rock under test when it is damaged from the data signal through a preset threshold and transmits it to the signal analysis device.

4. The rock failure joint early warning method based on β and b values ​​according to claim 1, characterized in that, The calculation of the most correlation coefficient between the β value and the b value, and the compensation of the b value to the β value through a compensation function to obtain the early warning coefficient, thereby realizing the early warning of damage to the rock under test, includes: By shifting the time axis of the b-value to the time axis of the β-value to form different time differences, the correlation coefficient between the b-value and the β-value under different time differences is calculated, and the compensation time difference is determined by the maximum correlation coefficient. Based on the compensation time difference, the b value is compensated to the β value using a compensation function to obtain the early warning coefficient, thereby realizing the early warning of damage to the rock under test.

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